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SnapHiC2: A computationally efficient loop caller for single cell Hi-C data

Single cell Hi-C (scHi-C) technologies enable the study of chromatin spatial organization directly from complex tissues at single cell resolution. However, the identification of chromatin loops from single cells is challenging, largely due to the extremely sparse data. Our recently developed SnapHiC...

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Detalles Bibliográficos
Autores principales: Li, Xiaoqi, Lee, Lindsay, Abnousi, Armen, Yu, Miao, Liu, Weifang, Huang, Le, Li, Yun, Hu, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168059/
https://www.ncbi.nlm.nih.gov/pubmed/35685374
http://dx.doi.org/10.1016/j.csbj.2022.05.046
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author Li, Xiaoqi
Lee, Lindsay
Abnousi, Armen
Yu, Miao
Liu, Weifang
Huang, Le
Li, Yun
Hu, Ming
author_facet Li, Xiaoqi
Lee, Lindsay
Abnousi, Armen
Yu, Miao
Liu, Weifang
Huang, Le
Li, Yun
Hu, Ming
author_sort Li, Xiaoqi
collection PubMed
description Single cell Hi-C (scHi-C) technologies enable the study of chromatin spatial organization directly from complex tissues at single cell resolution. However, the identification of chromatin loops from single cells is challenging, largely due to the extremely sparse data. Our recently developed SnapHiC pipeline provides the first tool to map chromatin loops from scHi-C data, but it is computationally intensive. Here we introduce SnapHiC2, which adapts a sliding window approximation when imputing missing contacts in each single cell and reduces both memory usage and computational time by 70%. SnapHiC2 can identify 5 Kb resolution chromatin loops with high sensitivity and accuracy and help to suggest target genes for GWAS variants in a cell-type-specific manner. SnapHiC2 is freely available at: https://github.com/HuMingLab/SnapHiC/releases/tag/v0.2.2.
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spelling pubmed-91680592022-06-08 SnapHiC2: A computationally efficient loop caller for single cell Hi-C data Li, Xiaoqi Lee, Lindsay Abnousi, Armen Yu, Miao Liu, Weifang Huang, Le Li, Yun Hu, Ming Comput Struct Biotechnol J Research Article Single cell Hi-C (scHi-C) technologies enable the study of chromatin spatial organization directly from complex tissues at single cell resolution. However, the identification of chromatin loops from single cells is challenging, largely due to the extremely sparse data. Our recently developed SnapHiC pipeline provides the first tool to map chromatin loops from scHi-C data, but it is computationally intensive. Here we introduce SnapHiC2, which adapts a sliding window approximation when imputing missing contacts in each single cell and reduces both memory usage and computational time by 70%. SnapHiC2 can identify 5 Kb resolution chromatin loops with high sensitivity and accuracy and help to suggest target genes for GWAS variants in a cell-type-specific manner. SnapHiC2 is freely available at: https://github.com/HuMingLab/SnapHiC/releases/tag/v0.2.2. Research Network of Computational and Structural Biotechnology 2022-06-01 /pmc/articles/PMC9168059/ /pubmed/35685374 http://dx.doi.org/10.1016/j.csbj.2022.05.046 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Li, Xiaoqi
Lee, Lindsay
Abnousi, Armen
Yu, Miao
Liu, Weifang
Huang, Le
Li, Yun
Hu, Ming
SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
title SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
title_full SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
title_fullStr SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
title_full_unstemmed SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
title_short SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
title_sort snaphic2: a computationally efficient loop caller for single cell hi-c data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168059/
https://www.ncbi.nlm.nih.gov/pubmed/35685374
http://dx.doi.org/10.1016/j.csbj.2022.05.046
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